linear_regression

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does (linear regression) and returns (slope, intercept, R²), but lacks critical details such as input format expectations (e.g., numeric arrays vs. strings), error handling, computational limits, or assumptions (e.g., linearity). This is insufficient for a tool with parameters and no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, efficiently conveying the core functionality in a single sentence. It avoids unnecessary details and is structured to immediately inform the user about the tool's purpose and outputs. However, it could be slightly improved by integrating parameter hints, but it remains highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a statistical tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It covers the basic operation and outputs but misses essential context such as parameter explanations, error conditions, and behavioral traits. This makes it inadequate for reliable tool invocation by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, with two required parameters ('xs' and 'ys') of type array of strings. The description does not explain what these parameters represent (e.g., independent and dependent variables), their expected format (e.g., numeric strings), or any constraints (e.g., equal length arrays). This leaves the semantics unclear, failing to compensate for the schema's lack of documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Perform linear regression to fit y = mx + b and return slope, intercept, and R²'. It specifies the statistical operation (linear regression), the model (y = mx + b), and the outputs (slope, intercept, R²). However, it does not explicitly differentiate from sibling tools (e.g., 'correlation' or other regression-related tools), which prevents a score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions the domain (statistics) and category (general), but offers no explicit context, prerequisites, or comparisons to sibling tools like 'correlation' or other statistical methods. This lack of usage direction limits its effectiveness for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.